Keep Calm and Learn Multilevel Linear Modeling: A Three-Step Procedure Using SPSS, Stata, R, and Mplus

Abstract

This piece is meant to help you understand and master two-level linear modeling in an accessible, swift, and fun way (while being based on rigorous and up-to-date research). It is divided into four parts: PART 1 presents the three key principles of two-level linear modeling. PART 2 presents a three-step procedure for conducting two-level linear modeling using SPSS, Stata, R, or Mplus (from centering variables to interpreting the cross-level interactions). PART 3 presents the results from a series of simulations comparing the performances of SPSS, Stata, R, and Mplus. PART 4 gives a Q&A addressing multilevel modeling issues pertaining to statistical power, effect sizes, complex design, and nonlinear two-level regression. The empirical example used in this tutorial is based on genuine data pertaining to ʼ90s and post-ʼ00s boy band member hotness and Instagram popularity. In reading this paper, you will have the opportunity to win a signed picture of Justin Timberlake.

Keywords

• linear regression
• multilevel modeling
• grand- and cluster-mean centering
• slope residual variance and covariance terms
• cross-level interaction
• confidence intervals
• simulations
• three-step procedure
• justin timberlake